Identifying suspicious mass regions in digitized mammograms is a first step in computer-aided diagnosis (CAD) schemes. In mammographic mass detection, two general approaches to the problem have been explored, namely single-image segmentation and image subtraction of two similar views of the two contralateral breasts (bilateral image subtraction). The first approach involves a variety of image segmentation techniques and generally relies on image density patterns. The second approach uses bilateral subtraction of corresponding left-right matched image pairs and depends on asymmetry between density patterns of the two images. The need for accurate registration between the images for bilateral subtraction has been noted. In addition, bilateral subtraction is not applicable in cases where bilateral images appropriate for subtraction are unavailable for a variety of clinical reasons.
Success of these techniques typically is measured by the average number of false-positive regions found per image in order to detect a given percentage of true positive regions.
Current computerized detection schemes tend to segment a large number of suspicious regions in order to achieve the sensitivity required for clinical utility. Reducing the number of suspicious regions identified during the initial step of any CAD scheme is important to its ultimate performance.